ABSTRACT

We wished to delineate granulocytes' impact on the clearance of different bacterial burdens of Pseudomonas aeruginosa and Staphylococcus aureus in a granulocyte-replete mouse thigh infection model. A mouse thigh model was employed. Bacterial challenges from 105 to 3 × 107 CFU (S. aureus) and from 3 × 104 to 3 × 108 CFU (P. aeruginosa) were injected into murine posterior thighs. Organism quantitation was at baseline, 2 h (Pseudomonas only), and 24 h. A Michaelis-Menten population model was fit to the data for each organism. Breakpoints for microbial containment by granulocytes were identified. Bacterial burdens exceeding that breakpoint value resulted in organism multiplication. The Michaelis-Menten model fit the data well. For P. aeruginosa, the observed-predicted plot had a regression equation that explained over 98% of the variance (P ≪ 0.001). For S. aureus, this relationship explained greater than 94% of the variance (P ≪ 0.001). Maximal growth rate constants, maximal population burdens, and the bacterial loads at which granulocytes killed if half-saturated were not different. The kill rate constant for P. aeruginosa was almost 10 times that of S. aureus. Bacterial kill by granulocytes is saturable. No difference between saturation points of different isolates was seen. A higher bacterial burden means an increasing reliance on chemotherapy to drive bacterial clearance.

Humans are endowed with an immune system that protects them from a vast number of infectious assaults. When dealing with many (but not all) bacterial infections, the granulocyte plays a key role in the clearance of the infection.

With the advent of antibiotics, we have become somewhat complacent about our ability to deal with serious bacterial infections. A great multiplicity of antibiotics of different classes and types within classes have been discovered and, after appropriate review by the Food and Drug Administration, have made their way into the clinician's armamentarium.

One issue not well addressed is the question of how much of the ability to cure infections is due to the antibiotics and how much is due to granulocytes. While the scientific community has developed extensive literature corresponding to many animal model systems in which the ability of an antibiotic to kill bacteria at the primary infection site has been linked to drug exposure (2, 4, 11, 12), little has been done to examine this issue in vivo relative to the granulocytes. Indeed, the vast majority of this literature has been developed in a setting in which the animals were rendered severely neutropenic by cyclophosphamide.

In this investigation, we examined the impact of granulocytes on bacterial cell clearance in a mouse thigh infection model. We looked at two common and important bacterial pathogens, Pseudomonas aeruginosa and Staphylococcus aureus.

We also felt it important to examine the impact of bacterial burden upon the clearance of the bacterial pathogen. Clinicians have long known of the importance of bacterial load (3) in terms of its effect on the likelihood of a good clinical outcome. We are unaware, however, of a quantitative analysis relating bacterial burden to the clearance of the pathogens by granulocytes.

MATERIALS AND METHODS

Microorganisms.A methicillin-susceptible Staphylococcus aureus strain (ATCC 29213) and Pseudomonas aeruginosa ATCC 27853 were used. Stocks of the microbes were stored in skim milk at −80°C. The isolate was subcultured on blood agar plates twice before each experiment.

Animals.Female, 24- to 26-g outbred Swiss-Webster (Taconic Farms, NY) mice were used in all in vivo studies. They received food and water ad libitum. All animal experimentation procedures were approved by and conducted in accordance with the guidelines of our Institutional Animal Care and Use Committee (IACUC).

Murine thigh infection model.A mouse thigh infection model pioneered by Eagle et al. (6) and greatly expanded upon by Craig et al. (2, 4, 5) was adapted to examine the relationship between granulocyte presence and reduction in the density of bacteria in thigh muscles of mice. These experiments were performed in normal mice.

Growth/death determination studies.Inocula at six different levels of Staphylococcus aureus ATCC 29213 ranging from 105 through 3 × 107 were injected into the posterior thighs of mice. For Pseudomonas aeruginosa ATCC 27853, inocula ranging from 3 × 104 through 3 × 108 were used. Bacterial challenges were verified by quantitative culture performed at the time of inoculation. At 0, 2, and 24 h after bacterial inoculation (for Pseudomonas aeruginosa), five mice per bacterial inoculum group were sacrificed by CO2 asphyxiation to establish the number of organisms. At 0 and 24 h (Staphylococcus aureus), cohorts (n = 5) of animals were sacrificed to determine the CFU/g body weight present. Both posterior thighs were carefully dissected and homogenized in ice-cold 0.9% saline (10:1, vol/wt). The homogenates of infected thigh muscles were quantitatively cultured.

Modeling methods.To mathematically determine the kill of microorganisms by granulocytes, the inhomogeneous differential equation (shown below) was used to describe the time course of organisms at the mouse thigh primary infection site. Multiple inocula were simultaneously comodeled by use of the big nonparametric adaptive grid population modeling program of Leary, Jelliffe, Schumitzky, and Van Guilder (BigNPAG) (9). Weights were the inverse of the observation variance for any cohort by the following equation:
$$mathtex$$\[dX_{1}/dt{=}K_{\mathrm{max}-\mathrm{growth}}{\times}[1{-}(X_{1}/\mathrm{POPMAX})]{\times}X_{1}{-}[260{\times}K_{\mathrm{max}-\mathrm{kill}}{\times}X_{1}/(K_{m}{+}X_{1})]{\times}X_{1}\]$$mathtex$$(1) where X1 is the number of organisms present in the mouse thigh at time t, POPMAX is the theoretical stationary-phase maximal number of organisms, Kmax-growth is the maximum growth rate, and Kmax-kill and Km are the Michaelis-Menten constants, where Kmax-kill is is the maximal kill rate induced by granulocytes and Km is the number of organisms per g of tissue at which the granulocytes are half-saturated. The number 260 is the average number of granulocytes/μl present at baseline (determined experimentally) that multiplies Kmax-kill. The baseline number of organisms were placed into the tissue compartment as an initial condition, which was a random variable and fit as part of the modeling process.

Goodness of fit was determined by examining observed-predicted plots, as well as determining the estimates of bias and precision for the regression. Mean weighted error was the measure of bias, and bias-adjusted precision was calculated.

RESULTS

Colony counts over time for different challenges of Pseudomonas aeruginosa in the thigh muscles of immunocompetent mice.In Fig. 1 A, we display the colony counts of P. aeruginosa in the mouse thigh at 0, 2, and 24 h. The initial burden injected into the posterior thigh ranged from 3 × 104 to 3 × 108 CFU. At the 2-h evaluation, the two lowest burdens decreased from the baseline, while the others showed net growth. At 24 h, the initial challenges of 3 × 104 through 3 × 105 demonstrated net kill by the granulocytes of approximately 1.1 to 2.0 log10 (CFU/g). The challenge of 1 × 106 achieved net stasis. All other challenges showed net growth of 2.2 log10 (CFU/g) growth or greater from baseline to 24 h [the highest inoculum grew only 1.7 log10 (CFU/g), because stationary phase was attained (POPMAX)].

(A) Time course of P. aeruginosa ATCC 27853 in mouse thigh (granulocyte replete) as a function of baseline bacterial burden. (B) Change in bacterial burden from 0 to 24 h.

The changes in bacterial burden for all challenges are shown in Fig. 1B.

Application of the mathematical model to the Pseudomonas aeruginosa data.The model fit the data well (Fig. 2). After the Bayesian step, the predicted-observed plot demonstrated a regression line with the following equation: Observed = 0.977 × Predicted + 0.104 (r2 = 0.989; P ≪ 0.001). As can be seen by inspection, the regression was adequately precise and was unbiased.

Predicted-observed plot after the Bayesian step for P. aeruginosa ATCC 27853 and point estimates of the parameter values.

Table 1 shows the point estimates of the parameters and their dispersions. The organism grew well, as demonstrated by the estimate of the maximal growth rate (Kmax-growth), which was 4.299 h−1. The estimated maximal population count was physiologic at 1.07 × 1010 CFU/g. The estimate of the maximal kill rate induced by granulocytes (Kmax-kill) was 1.295 h−1. It should be noted that this number needs to be multiplied by the baseline granulocyte count, which for these experiments was 260/μl. This was determined separately in a preexperiment in this laboratory. The number of organisms that half-saturated the granulocytic kill ability was 4.30 × 106 CFU/g. This number is believable, as the initial challenge of 1 × 106 CFU shown in Fig. 1 (resulting in 6.9 × 105 CFU/g at time zero) shows near net stasis [actually 0.15 log10 (CFU/g) kill] at 24 h.

Point estimates and dispersions of the parameter values for P. aeruginosa ATCC 27853 growth in the mouse thigh and the kill by granulocytesa

Colony counts over time for different challenges of Staphylococcus aureus in the thighs of immunocompetent mice.In Fig. 3 A, we display the colony counts of S. aureus in the mouse thigh at time zero and 24 h. The initial burden injected into the posterior thigh ranged from 1 × 105 to 3 × 107 CFU. In this evaluation, data at the 2-h time point were not obtained, as they were relatively noninformative. At 24 h, the initial challenges of 1 × 105 and 5 × 105 demonstrated a net kill by the granulocytes of approximately 1.0 log10 (CFU/g). The challenges of 1 × 106 and 3 × 106 achieved net stasis. The other two challenges showed net growth of 1.55 and 1.45 log10 (CFU/g) increase from baseline to 24 h.

(A) Time course of S. aureus ATCC 29213 in mouse thigh (granulocyte replete) as a function of baseline bacterial burden. (B) Change in bacterial burden from 0 to 24 h.

The changes in bacterial burden for all challenges are shown in Fig. 3B.

Application of the model to the Staphylococcus aureus data.The model fit the data well (Fig. 4). After the Bayesian step, the predicted-observed plot demonstrated a regression line with the following equation: Observed = 0.917 × Predicted + 0.679 (r2 = 0.947; P ≪ 0.001). As can be seen by inspection, the regression was adequately precise and was unbiased.

Predicted-observed plot after the Bayesian step for S. aureus ATCC 29223 and point estimates of the parameter values.

Table 2 shows the point estimates of the parameters and their dispersions. The organism grew well, as demonstrated by the estimate of maximal growth rate (Kmax-growth) of 5.678 h−1, which represents slightly but not significantly faster growth than observed with P. aeruginosa. The estimated maximal population count was physiologic at 4.24 × 1010 CFU/g, again, slightly but not significantly different from that seen with the strain of Pseudomonas studied. The estimate of the maximal kill rate induced by granulocytes (Kmax-kill) was 0.115 h−1 (Table 2), which is considerably less than (∼1/10) that observed for P. aeruginosa. Because of the variability identified, this number is not significantly different from the value identified for Pseudomonas. The number of organisms that half-saturate the granulocytic kill ability was 5.55 × 106 CFU/g. This number is believable, as the initial challenges of 1 × 106 and 3 × 106 CFU shown in Fig. 3 (resulting in 3.55 × 106 and 1.35 × 107 CFU/g at time zero, respectively) show approximate net stasis at 24 h.

Point estimates and dispersions of the parameter values for the growth of S. aureus ATCC 29223 in the mouse thigh and the kill of the organism by granulocytesa

DISCUSSION

In the therapy of infections, antibiotics are a crucial tool to optimize therapeutic outcome. It is also clear that many people survived serious infections in the preantibiotic era because of their immune function. For most typical bacterial infections, granulocytes play a central role in control of the infection.

Here we have examined a strain of Pseudomonas aeruginosa and a strain of methicillin-susceptible Staphylococcus aureus in a nonneutropenic mouse thigh infection model. The first finding from the studies was the impact of increasing the bacterial burden on the adequacy of the response of the granulocytes. For Pseudomonas aeruginosa (Fig. 1A and B), a bacterial challenge of 1 × 106 CFU (resulting in 6.92 × 105 CFU/g at baseline) resulted in stasis, whereas 3 × 106 CFU as a challenge (resulting in 2.19 × 106 CFU/g at baseline) lost control and grew to 3.16 × 108 CFU/g at 24 h. For Staphylococcus aureus (Fig. 3A and B), a challenge of 3 × 106 (1.35 × 107 CFU/g at baseline) achieved stasis (net granulocyte kill balanced S. aureus growth), whereas with a 1 × 107-CFU challenge (4.52 × 107 CFU/g), net microbial containment was lost and colony counts exceeded 9 log10 (CFU/g) at 24 h. For both isolates, then, baseline challenges of 3 × 106 CFU (resulting in 2.19 × 106 CFU/g) to 1 × 107 (resulting in 4.52 × 107 CFU/g) overwhelmed the system, resulting in net growth to near maximal values of organisms over 24 h.

Examination of Fig. 1 and 3 resulted in a preliminary conclusion that the kill of bacterial pathogens by granulocytes followed Michaelis-Menten kinetics and therefore was saturable. It should be noted that Leijh et al. (10) demonstrated granulocyte saturability for Staphylococcus aureus and Escherichia coli in a set of in vitro experiments previously. We wrote a mathematical model in which bacterial growth was opposed by granulocyte killing but where the system was explicitly saturable. For both the P. aeruginosa data and the S. aureus data (Fig. 2 and 4), the model fit the data quite well and, in both instances, explained greater than 94% of the variance.

The model parameters (Tables 1 and 2) showed very similar rates of maximal growth (Kmax-growth) for both organisms and similar maximal population values (POPMAX). For the Michaelis-Menten parameters, the colony counts that half-saturated granulocyte killing were strikingly similar, at 4.3 × 106 CFU/g for Pseudomonas and 5.55 × 106 for Staphylococcus. The only major difference was seen with the maximal kill rate term (Kmax-kill), with the term for Pseudomonas being about 10-fold higher than that for Staphylococcus. We speculate that this may be because of the well-known ability of Staphylococcus aureus to survive within granulocytes (7) and is likely to explain the observed difference in maximal 24-h kill of 1 log10 (CFU/g) for S. aureus versus 2 log10 (CFU/g) for P. aeruginosa.

These findings may also help explain the observation by clinicians that there are some antimicrobial agents that, at an approved dose and schedule, are not particularly potent, yet patients with less-severe community-acquired infections frequently recover from infection. It is important to state that the following is speculation and that these findings on saturability and, more importantly, the degree of cell kill per day need to be repeated in the more appropriate murine pneumonia model. The Food and Drug Administration (1, 14) has started to question the validity of clinical trials of community-acquired pneumonia (CAP) in which the bulk of the patients enrolled were of mild severity (PORT I and PORT II [severities defined by the Pneumonia Patient Outcomes Research Team; the scale is also referred to as Pneumonia Severity Index or PSI]). Their hypothesis is that the actual recovery is due more to the patients' immune systems (especially the granulocytes) and less to the effect of the antimicrobial.

Generally, less-severe community-acquired infections (using PORT I and PORT II CAP patients as an example) have lower bacterial burdens, and the pulmonary infiltrates seen from chest X-rays are very infrequently multilobar. If the bacterial burden is around the value at which granulocyte kill is half saturated, then relatively small amounts of drug effect are sufficient to move the total burden below this value, and the granulocyte kill can start the process of clearing the infection.

Alternatively, patients with greater bacterial burdens and with multilobar involvement will rapidly saturate their granulocytes, and in the absence of a major antimicrobial effect, net growth of the bacteria will occur with resultant failure of therapy. Treatment of PORT IV and PORT V CAP patients (that is, with CAP of greater severity) with borderline antimicrobial agents is likely to lead to an unacceptable rate of therapeutic failure.

It is important to note that, in the case of Pseudomonas aeruginosa, patients with ventilator-associated pneumonia (VAP) often have their diagnosis made by means of quantitative bronchoalveolar lavage, for which the smallest burden meeting the VAP definition is 104 CFU/ml. The dilution associated with bronchoalveolar lavage is on the order of 30- to 100-fold (seen explicitly when modeling drug penetration into epithelial lining fluid (ELF) by using a urea correction) (13). Consequently, the true concentration is 3 × 105 to 1 × 106 per ml. At a very small volume of 100 ml (many VAP patients would have close to 1 liter of organisms or more), the total bacterial burden would exceed 3 × 107 per ml and would saturate the granulocytes. This is a case where only the most potent antimicrobial therapy will drive a good clinical outcome.

The next step is to delineate how antimicrobial agents and the immune system can work together to attain optimal clinical responses. Previous work has shown that even subinhibitory drug concentrations can help with intracellular killing (7). Our laboratory (8) has previously modeled the direct interaction of granulocytes and amphotericin B for clearance of candidal infection. This should be done with a range of bacteria and different antimicrobial agents. Finally, we focused on granulocytes in this investigation, but it will be increasingly important to understand what other parts of the immune system add to clearance of infection.

ACKNOWLEDGMENTS

This work was supported by R01AI079578, a grant from NIAID to the Emerging Infections and Pharmacodynamics Laboratory. We have no conflicts to disclose.

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